We note that, in Table REF , we reported an SSIM of \(0.9270\)  and a PSNR of \(36.22\)  for our method, while in Table REF , we reported an SSIM of \(0.9291\)  and a PSNR of \(36.39\) . In order to boost the performance of our method in accordance with the observed differences between Tables REF  and REF , we employed the self-ensemble strategy used by Lim et al. [1]}. For each input image, the self-ensemble strategy consists in generating additional images using geometric transformations, e.g. rotations and flips. Following Lim et al. [1]}, we generated 7 augmented images from the LR input image, upsampling all 8 images (the original image and the 7 additional ones) using our CNN. We then applied the inverse transformations to the resulting 8 HR images in order to obtain 8 output images that are aligned with the ground-truth HR images. The final output image is obtained by taking the median of the HR images. In the following experiments on CH and NAMIC data sets, the reported results always include the described self-ensemble strategy.
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